Datacenters are large clusters of components (e.g., hardware and/or software resources) that are connected to perform operations using massive amounts of data. Storing and retrieving metrics received from these components is a complex task. In addition, as the amount of data grows, system performance and efficiency becomes paramount. One traditional method is to rely on a NoSQL distributed database management system. However, these systems have a number of associated problems. For example, NoSQL distributed database management systems do not allow for easy onboarding of new components to the datacenter. This is because a redesign of the database is required every time a new component of the datacenter is added. As datacenters continually grow, adding new components to increase capacity within the datacenter, the cost of human resources required to redesign the database increases significantly.
While alternatives to NoSQL distributed database management systems exist, they too suffer from a number of drawbacks. For instance, these systems rely heavily on expending computing resources, e.g., cache resources. This is especially true for the storage and retrieval of large amounts of data. As usage of large systems and infrastructures increases the amount of data to be stored, it is advantageous to employ methods and systems that allow for the efficient storage and retrieval of data. Even more, enhanced methods and systems are needed to increase the flexibility of onboarding new components to the datacenter without taking the time and expense currently required to re-design the database(s). Attempts have been made to provide a technological solution to overcome these deficiencies, but conventional technology has generally failed to provide an adequate solution. As such, the present disclosure is meant to address the deficiencies of the prior technology.